Search Results (603)

The genus Oncosiphon (Asteraceae), consisting of aromatic herbs, is indigenous to southern Africa. Oncosiphon species have been documented in Khoi-San ethnobotany as herbal remedies for typhoid fever, pneumonia, and as diuretics. Research on the biological properties and comprehensive phytochemical profiling of these important Oncosiphon species is currently limited. This study was therefore undertaken to address the knowledge void in chemical profiling, through the application of various analytical techniques to analyse the volatile and non-volatile constituents of three South African Oncosiphon species. The aerial parts of Oncosiphon suffruticosus (n = 28), O. grandiflorus (n = 16), and O. africanus (n = 4) were collected from various locations in the Western Cape Province of South Africa. The stems and leaves (SL) were separated from the flowers (F) and analysed as distinct samples. The methanol: chloroform (1:1, v/v) extracts were prepared and analysed using ultra–high–performance liquid chromatography quadrupole time-of-flight time–of–flight mass spectrometry (UHPLC–QToF–MS) and a semi–automated high–performance thin–layer chromatography (HPTLC) system. Multivariate data analysis was performed on the UHPLC–QToF–MS data to determine interspecies chemical variation. Two-dimensional (2D) gas chromatography (GCxGC–ToF–MS) was used to determine the headspace volatile profiles of the intact aerial parts. The results show that the non-volatile profiles of the Oncosiphon species are characterised by amino acids, phenolic acids, flavonoids, sesquiterpene lactones, and fatty acid derivatives. The HPTLC profiles of O. grandiflorus and O. africanus are chemically more closely related, and O. suffruticosus has a distinct profile, which is supported by the chemometrics results of the flowers. The major headspace volatile compounds in Oncosiphon flowers are α-pinene, α-ocimene, eucalyptol, o-cymene, and artemisia alcohol, whereas the stems and leaves mainly consist of α-ocimene, eucalyptol, and yomogi alcohol. Full article

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. Full article

Dual-propulsion UAVs employ separate rotors for rotary-wing and fixed-wing modes to achieve VTOL (vertical take-off and landing) and high-speed cruise. This paper analyzes steady climb in high-speed flight by utilizing the redundant rotary-wing rotors. We develop the models of aerodynamic forces and thrust forces of a dual-propulsion UAV to obtain its longitudinal dynamic model. The maneuverability of the UAV is analyzed based on the dynamic model to reveal whether a steady climb at a given climb angle is possible within allowable thrust forces. The analytical results show that the climb flight performance of the UAV can be enhanced by utilizing the redundant control inputs during high-speed flights. Flight experiments not only demonstrate that several climb flight states predicted by the analysis are successfully realized, but also that steady climb at a higher climb angle, unattainable in conventional fixed-wing mode, is made possible by simultaneously using the rotors for rotary-wing mode. The enhanced flight performance would increase the number of missions that the UAV can accomplish. Full article

The purported benefits of Virtual Reality for pilot flight simulator training, such as increased immersion and presence, would be of great benefit in training those flight skills that rely on visuospatial awareness. The implementation of this technology for the training of pilots requires careful consideration of its ability to transfer required skills and of any comparative advantages over conventional flight simulators. In order to examine this question, a quasi-transfer-of-training study was conducted using a separate-sample pretest–posttest design. The ability of a low-cost VR simulator to transfer flying skills and mission projection skills, using internally valid measures, during a common flight manoeuvre was evaluated. Results were consistent with improved post-intervention flying performance (g = 0.875) and ‘mission projection’ performance (g = 0.661), with no statistically significant difference between the estimated effect sizes, as well as the combined measure (g = 0.768). The findings indicate that the VR simulator was associated with better performance in the quasi-transfer of basic flying skills, those skills that require understanding of spatial relationships based on visual information, and in the broader training of technique. These findings must, however, be considered in the context of the noted limitations of the technology and the research design. Full article

To address the issues of safety risk analysis and conflict assessment for integrated flight of manned aircraft and fixed-wing unmanned aerial vehicles (UAVs) in low-altitude mixed-operation airspace, this study enhances the foundational Event model. By incorporating UAV characteristics such as geometric features and aerodynamic mechanisms, alongside design dimensions and onboard performance metrics, an improved collision risk model is developed—the Enhanced Event-Based Framework for Multidimensional Geometry and Quasi-Monte Carlo Analysis of Flight Performance (EMGF-M). This enhancement rectifies the limitations of the basic model regarding parameter coverage and scenario adaptability, thereby improving the reliability and validity of the computational results. Experimental results demonstrate that, in accordance with the target safety level for airspace conflicts set by the International Civil Aviation Organization (ICAO), the application of the improved Event collision model yields quantifiable assessments of safety risks and safe separation distances for integrated operations in low-altitude mixed-use airspace. Utilizing these computational results for integrated flight procedure design at a general airport in Southwest China, the study shows that the air traffic flow in the low-altitude mixed-operation airspace increased from 9.2 to 20.9 operations per hour. The practical significance of this method lies in its guidance for accurately assessing safety risks in mixed airspace operations and for determining quantifiable separation minima for integrated flight trajectory planning. Full article

Efficient plant protection requires precise monitoring of spray droplets, yet current in situ methods for measuring in-flight droplet flow are limited. This study proposed a laser imaging-based method to quantify spray intensity without physical contact or tracers. An optimal imaging angle was determined via simulation by maximizing the linearity between the received optical feature and droplet volume density while satisfying geometric constraints. A compact acquisition device was then developed and tested with eight nozzle specifications under fixed pressure. Image processing algorithms—including cropping, RGB channel separation, and binarization—were employed to extract pixel area and cumulative intensity, with gravimetric measurements serving as the reference. Results showed that under optimized exposure and gain settings, features from the green and blue channels exhibited a strong linear correlation with flow rate (R² = 0.93–0.97). Based on these findings, this study demonstrates that in-flight droplet flow rate can be directly quantified from image features—a departure from conventional deposition-based approaches. The proposed method enables rapid, non-contact spray assessment using only a camera and laser module, offering a low-cost, simple-structured solution for spray system optimization and field monitoring. Full article

Height anomaly, defined as the separation between the quasi-geoid and the reference ellipsoid, is fundamental to quasi-geoid refinement. While the Goddard Mars Model-3 (GMM-3) developed by NASA’s Goddard Space Flight Center (GSFC) and the JPL Mars gravity field MRO120D (JGMRO_120D) model developed by NASA’s Jet Propulsion Laboratory (JPL) stand as two representative Martian gravity field models, the systematic differences between them and their associated physical implications remain insufficiently quantified. This study establishes a validated computational framework for Martian height anomaly determination using updated physical parameters and spherical harmonic expansions. Validation against terrestrial datasets confirms high reliability (standard deviation: 0.0695 m relative to International Centre for Global Earth Models (ICGEM)), ensuring confidence in subsequent analysis. Our analysis reveals three critical findings: (1) Systematic latitudinal biases between GMM-3 and JGMRO_120D exhibit a monotonic gradient from −1.3 m near the equator to +3.9 m at the North Pole, suggesting differential parameterization of polar mass loading or tidal models between the two centers. (2) Polar clustering of uncertainties and outliers exceeding the 95th percentile (>7 m) concentrate non-randomly at latitudes >60°, which is attributed to sparse satellite tracking and seasonal ice cap modeling limitations. (3) There is error amplification in lowland terrains, where relative errors exceed 60% in flat regions (near-zero anomalies), posing critical risks for precision landing missions. While global consistency between models is high (R² = 0.9999), the identified discrepancies provide new constraints on Mars’s geophysical models and essential guidance for future gravity field improvements and mission planning. Full article

Urban Air Mobility (UAM) introduces new safety challenges as small unmanned aircrafts begin to operate at high density in complex urban environments. Traditional air traffic management (ATM) systems developed for manned aviation are unable to accommodate the autonomy, mission diversity, and dynamic obstacle conditions typical of low-altitude operations. This review examines recent research on mid-air collision risk and airspace safety modeling for UAM and identifies key challenges in adapting existing safety concepts to small-scale and autonomous flight. The study compares international management frameworks of the United States, Europe, and China. Then analyzes representative airspace structures such as Free, Layered, Zoned, and Pipeline configurations. It further reviews deterministic and probabilistic separation models, geometric and optimization-based avoidance strategies, and structured airspace approaches such as the virtual-tube concept for coordinated swarm navigation. The findings highlight the lack of integrated models that couple human, energy, and communication factors into quantitative risk assessment. The paper concludes by outlining future research needs in uncertainty modeling, digital-twin simulation, and interoperability to support safe and scalable UAM development. Full article

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

Feline conjunctivitis is a common ocular disorder; however, the molecular composition of feline tear fluid and its alterations during ocular surface inflammation remain poorly characterized. This pilot study aimed to explore the tear proteome of cats with conjunctivitis using matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI–TOF/TOF MS) and to compare findings with those from clinically healthy cats. Tear samples were collected using Schirmer tear test strips from healthy cats and cats diagnosed with conjunctivitis. Total protein concentration was measured by ultraviolet absorbance spectrophotometry, and tear proteins were separated by SDS–polyacrylamide gel electrophoresis, followed by in-gel trypsin digestion and MALDI–TOF/TOF MS analysis. Nine distinct tear proteins were identified, including antimicrobial and immune-related components such as lactoperoxidase, lactotransferrin, albumin, and immunoglobulin A constant region. Lactoperoxidase and SBP1 were identified in feline tear fluid for the first time. No proteins uniquely associated with conjunctivitis were detected. The mean total tear protein concentration was numerically higher in cats with conjunctivitis (13.06 ± 0.75 mg/mL) than in healthy cats (9.69 ± 0.67 mg/mL); however, this difference did not reach statistical significance (p = 0.095) and should be interpreted cautiously given the limited sample size. This pilot study provides preliminary insights into tear protein profiles in cats with conjunctivitis and highlights the need for larger quantitative investigations. These findings provide a preliminary framework for future studies aimed at further characterizing molecular alterations associated with feline ocular surface disorders. Full article

Bloodstream infection (BSI) is a serious clinical condition associated with high morbidity and mortality, requiring rapid identification of causative agents and timely antimicrobial susceptibility testing (AST). This study evaluated accelerated bacterial identification from positive blood culture samples using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with two rapid processing approaches: a serum separation tube-based centrifugation method (SST method) and shortened cultivation on solid media. Rapid identification was followed by accelerated AST, performed either from a bacterial cell pellet (SST method) and from early-grown bacterial biomass (shortened cultivation protocol). The results were compared with those obtained using routine laboratory procedures. A total of 270 positive blood culture samples were analyzed, with 135 samples processed by each protocol. Both approaches achieved an identification success rate of 93.33%. Rapid AST using the SST method showed error rates of 0.51% minor errors, 0.57% major errors, and 0.23% very major errors, with an overall agreement of 98.69%. The shortened cultivation protocol demonstrated lower error rates (0.46% minor errors and 0.23% major errors) and an overall agreement of 99.31%. These findings confirm that MALDI-TOF MS enables reliable early identification of BSI pathogens and rapid AST, supporting timely optimization of antimicrobial therapy and early detection of multidrug-resistant strains. Full article

The marine centrifugal pump is one of the most energy-intensive pieces of equipment in ship auxiliary machinery, and the efficient design of its hydraulic components can effectively reduce the total energy consumption of the ship system. Aiming at the complex three-dimensional twisted blade profile structure of the marine centrifugal pump, this paper optimized the clonal selection algorithm and constructed an automatic hydraulic optimization design method for the high-efficiency centrifugal pump impeller. Considering the multi-condition operation characteristics of the marine centrifugal pump, a performance test platform for the marine centrifugal pump was built, and the actual operating conditions of the model pump were tested to obtain its performance characteristics under operating conditions. The numerical simulation method was employed to capture and analyze the internal flow field and flow characteristics of the model pump. Addressing the design challenges of the marine centrifugal pump impeller, which involve multiple parameters with significant interactions, a traditional clonal selection algorithm was enhanced using a Slime Mold Algorithm, and a hybrid Clonal Selection Algorithm integrated with Slime Mold and Tangent Flight mechanisms was established. Based on the MATLAB and ANSYS platforms, an automated hydraulic optimization design framework for the centrifugal pump impeller was established. Using the optimized clonal selection algorithm, with the operational efficiency of the model pump as the optimization objective and controlling ten key geometric parameters of the blade profile through Bézier curves, the blade profile optimization design was achieved. The pump hydraulic efficiency under the rated flow condition increased by 7%. The unsteady internal flow efficiency of the optimized marine centrifugal pump was significantly improved. The blade optimization alleviated flow separation phenomena on the tangential surface of the impeller and in partial regions of the volute, reduced the flow loss area, and significantly decreased overall flow losses. Full article

Background/Objectives: The COVID-19 pandemic has emphasized the urgent need for non-invasive diagnostic strategies. While breath analysis has been widely investigated, sweat and sebum remain largely unexplored, despite being abundant, chemically diverse, and easily collected. This exploratory study presents a proof-of-concept workflow to evaluate their potential for infection biomarker discovery. Methods: Samples from 51 subjects were analyzed by headspace solid-phase microextraction coupled with gas chromatography and time-of-flight mass spectrometry (HS-SPME-GC/ToF-MS). Over 8000 untargeted volatile compounds were detected, reflecting the high complexity of these matrices. Results: Data refinement and chemometric modelling using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) revealed robust separation between SARS-CoV-2-positive Patients and Controls. Classification accuracies consistently exceeded 95%, demonstrating the robust discriminative performance of the approach. Among the detected volatiles, 2-methylbenzenemethanol acetate emerged as the most informative compound, representing a potential biomarker candidate. Conclusions: This work shows that the sweat and sebum volatilome can be exploited for clinical applications. The workflow integrates non-invasive sampling, comprehensive chromatographic profiling, and advanced statistical modelling, representing a methodological contribution to analytical chemistry. Beyond COVID-19, the strategy provides a potential framework for volatile organic compound (VOC)-based diagnostics across different diseases and supports future development of sensor technologies for translation into healthcare practice. Full article

Unmanned Aerial Vehicle (UAV) remote sensing has become essential for real-time earth observation applications, including precision agriculture, traffic monitoring, and disaster response. However, small-target detection in UAV aerial imagery still faces critical challenges: extreme scale variation due to variable flight altitudes, background interference from complex terrain, and insufficient pixel information for tiny objects. To address these issues, this work proposes FKIFM-DETR, a real-time transformer-based detection framework leveraging multi-domain information fusion. First, a Spatial-Frequency Fusion Module (SFM) is designed to integrate spatial and frequency-domain features for capturing fine-grained target details while suppressing background noise; second, a High–Low Frequency Block (HL-Block) is introduced to separately process high-frequency local details and low-frequency global context, balancing detail retention and semantic awareness; finally, a Channel Feature Recalibration-Enhanced Feature Pyramid Network (SPCR-FPN) is employed to strengthen the interaction between shallow spatial features and deep semantic features. On the VisDrone2019 dataset, FKIFM-DETR achieves 6.3% and 5.3% improvements in mAP@0.5 and mAP@0.5:0.95 over the RT-DETR baseline, respectively; evaluations on TinyPerson and HIT-UAV datasets further demonstrate its cross-scenario applicability. These results demonstrate the potential of FKIFM-DETR for practical UAV remote sensing applications such as crowd surveillance, vehicle tracking, and emergency rescue. Full article

Electromechanical Actuators (EMAs) are critical components in More-Electric Aircraft (MEA) and Reusable Launch Vehicles (RLVs), yet they remain vulnerable to jamming and fatigue failures under high-stress flight maneuvers. Existing Health-Aware Flight Control approaches often treat failure prediction and control allocation as separate processes, leading to suboptimal sortie generation rates. This paper presents a reliability-adaptive control framework that unifies trajectory tracking with online health management. Empowered by a hierarchical mission-to-control architecture, the system employs stochastic Model Predictive Control (SMPC) to actively modulate control surface deflection profiles in real time. A comparative case study on a coupled EMA drivetrain demonstrates that the proposed controller extends useful life by 65% compared to fixed-gain baselines, achieves 23% higher mission performance than reactive PID controllers, and it maintains zero constraint violations throughout the mission by optimally distributing the health budget across mission phases. Full article